Databricks-ML-Assoc Model Deployment Practice Question
An MLOps engineer is responsible for a Databricks Model Serving endpoint that serves a mission-critical pricing model. The team wants an automated safeguard that detects when live input feature distributions drift away from the training distribution and triggers a retraining workflow, without modifying the model artifact itself. Which Databricks capability should be configured?
⚠ Common exam trap
Candidates often confuse endpoint scaling or registry stage changes with monitoring, when only logged live payloads can reveal distribution shift.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable inference tables on the endpoint and build a Databricks SQL alert over the logged payloads.
Inference tables capture the actual request payloads hitting the endpoint into a queryable Delta table, which is the foundation for computing drift metrics against training baselines. Pairing that with a scheduled query or SQL alert creates an automated signal that can start a retraining job, all without touching the model artifact, unlike request rejection, capacity changes or registry stage bookkeeping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the endpoint's workload size so it can handle higher request volume.
Why it's wrong here
Workload size controls compute capacity per replica and therefore throughput and latency, not the statistical properties of incoming data. Scaling up the endpoint would not reveal distribution shift, and it does nothing to trigger retraining. This option addresses a performance symptom rather than the monitoring and alerting objective described in the stem.
- ✗
Add a custom preprocessing step to the model that rejects requests outside the training range.
Why it's wrong here
Hard-rejecting out-of-range requests changes serving behaviour and can block legitimate traffic, and it still requires modifying and re-registering the model, which the scenario explicitly rules out. It also conflates data quality gating with drift detection, since drift is a distributional shift rather than a single out-of-bounds value. This approach is both invasive and a poor fit for monitoring.
- ✓
Enable inference tables on the endpoint and build a Databricks SQL alert over the logged payloads.
Why this is correct
Inference tables persist the request and response payloads of the endpoint to a Delta table, which can be queried continuously. Comparing logged feature values against training statistics in SQL and raising an alert on the drift metric gives an automated trigger for retraining, and it requires no change to the model artifact. This directly matches the requirement for a non-invasive drift safeguard.
- ✗
Set the model version's stage to Production and rely on stage transitions to signal drift.
Why it's wrong here
Stages and transitions are lifecycle bookkeeping performed by humans or jobs; they carry no information about live feature distributions. Nothing about a stage transition detects drift, so this would never fire automatically when input data shifts. It also misuses registry metadata as a monitoring mechanism, which it was not designed to be.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-ML-Assoc exam.